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06/03/2023
Correcting auto-differentiation in neural-ODE training
Does the use of auto-differentiation yield reasonable updates to deep ne...
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05/21/2023
On optimal bases for multiscale PDEs and Bayesian homogenization
PDE solutions are numerically represented by basis functions. Classical ...
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02/09/2023
Differentially Private Optimization for Smooth Nonconvex ERM
We develop simple differentially private optimization algorithms that mo...
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12/09/2022
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization
Nonconvex optimization is central in solving many machine learning probl...
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01/19/2022
On the Complexity of a Practical Primal-Dual Coordinate Method
We prove complexity bounds for the primal-dual algorithm with random ext...
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11/25/2021
Low-rank approximation for multiscale PDEs
Historically, analysis for multiscale PDEs is largely unified while nume...
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11/03/2021
A reduced order Schwarz method for nonlinear multiscale elliptic equations based on two-layer neural networks
Neural networks are powerful tools for approximating high dimensional da...
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11/02/2021
Coordinate Linear Variance Reduction for Generalized Linear Programming
We study a class of generalized linear programs (GLP) in a large-scale s...
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04/19/2021
Randomized Algorithms for Scientific Computing (RASC)
Randomized algorithms have propelled advances in artificial intelligence...
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02/26/2021
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums
We study structured nonsmooth convex finite-sum optimization that appear...
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11/01/2020
Manifold Learning and Nonlinear Homogenization
We describe an efficient domain decomposition-based framework for nonlin...
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10/22/2020
Random Coordinate Underdamped Langevin Monte Carlo
The Underdamped Langevin Monte Carlo (ULMC) is a popular Markov chain Mo...
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10/03/2020
Random Coordinate Langevin Monte Carlo
Langevin Monte Carlo (LMC) is a popular Markov chain Monte Carlo samplin...
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05/28/2020
Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity
We study a model for adversarial classification based on distributionall...
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12/18/2019
Interleaved Composite Quantization for High-Dimensional Similarity Search
Similarity search retrieves the nearest neighbors of a query vector from...
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12/12/2019
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimization
We propose a communication- and computation-efficient distributed optimi...
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10/04/2019
Schwarz iteration method for elliptic equation with rough media based on random sampling
We propose a computationally efficient Schwarz method for elliptic equat...
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03/04/2018
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization
In this paper, we propose a communication- and computation- efficient di...
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01/24/2018
Training Set Debugging Using Trusted Items
Training set bugs are flaws in the data that adversely affect machine le...
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10/16/2017
Using Neural Networks to Detect Line Outages from PMU Data
We propose an approach based on neural networks and the AC power flow eq...
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09/26/2013
Online Algorithms for Factorization-Based Structure from Motion
We present a family of online algorithms for real-time factorization-bas...
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07/21/2013
On GROUSE and Incremental SVD
GROUSE (Grassmannian Rank-One Update Subspace Estimation) is an incremen...
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07/03/2012
Robust Dequantized Compressive Sensing
We consider the reconstruction problem in compressed sensing in which th...
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11/02/2011
Approximate Stochastic Subgradient Estimation Training for Support Vector Machines
Subgradient algorithms for training support vector machines have been qu...
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04/22/2011